class_docstr: Check how good the error approximations are. Of course, we use an
  approximation for P so this itself is an approximation.
class_docstr_tok: ["Check","how","good","the","error","approximations","are",".","Of","course",",","we","use","an","approximation","for","P","so","this","itself","is","an","approximation","."]
code: def __init__(self, P: csr_matrix):
      self.P = P.copy()
      self.exact_errors = []
      self.bh_errors = []
      self.fft_errors = []
  
file_path: openTSNE/openTSNE/callbacks.py
imports: import logging
  from functools import partial
  import numpy
  from scipy.sparse import csr_matrix
  from openTSNE import kl_divergence
  from openTSNE.tsne import TSNEEmbedding
label_desc: Use image node_id 1 to create a new ErrorApproximations object from inherited base classes: Callback with example: obj = ErrorApproximations(P)